Today’s tutorial highlights progress to date through an “end to end” workflow using a real world example of (1) getting, (2) manipulating and (3) enriching a hydrofabric.
If you successfully complete this tutorial, you will create the minimal set of data files (and skills to experiment!) needed for the AWI datastream and NGIAB.
This tutorial can be followed from this webpage which has complete discussion and text surrounding the respective code chunks, or, from the companion R script that can be found here.
Before you jump into this, ensure you have your environment set up by installing R as detailed here # Getting Started
# Install -----------------------------------------------------------------
# install.packages("remotes")
# install.packages("powerjoin")
remotes::install_github("NOAA-OWP/hydrofabric")Attach Package
# helper function to use throughout tutorial
make_map = function(file, pois) {
hf = read_hydrofabric(file)
mapview::mapview(hf$catchments) + hf$flowpaths + pois
}
### ---- Sample out files and source for today ---- ###
fs::dir_create("tutorial")
source <- '/Users/mjohnson/hydrofabric/'
reference_file <- "tutorial/poudre.gpkg"
refactored_file <- "tutorial/refactored.gpkg"
aggregated_file <- "tutorial/aggregated.gpkg"
nextgen_file <- "tutorial/poudre_ng.gpkg"
model_atts_file <- "tutorial/poudre_ng_attributes.parquet"
model_weights_file <- "tutorial/poudre_ng_weights.parquet"Building a NextGen Hydrofabric
Get Reference Fabric (subsetting)
For this example, we want to prepare a NextGen hydrofabric and
associated products for the area upstream of NWIS 06752260
that sits on the Cache La
Poudre River in Fort Collins, Colorado. Feel free to use any
particular location of your desire (as relevant to our subsetting
tools), we’ve set up the Lynker-Spatial
Hydrolocation Viewer to make finding an appropriate starting
reference point of interest (POI) easier This POI will define the most
downstream point in our network, and we’ll need to pull out and save
(subset) the reaches which drain to this point in order to collect the
network needed for the model domain.
The lynker-spatial hydrolocation inventory is both a subset and superset of the community POI set. Meaning, we use a subset of the community POIs, and add a selection needed for NextGen modeling. This include (but are not limited to) the NWS LIDs, Coastal/Terristrail instactions, NWM reservoirs and lakes, Coastal Gages, and more!
## --- Define starting feature by source and ID
## https://waterdata.usgs.gov/monitoring-location/06752260
## https://reference.geoconnex.us/collections/gages/items?provider_id=06752260
# Use get_subset to build a reference subset
get_subset(
hl_uri = "Gages-06752260",
source = using_local_example,
type = "reference",
hf_version = "2.2",
lyrs = c("divides", "flowlines", "network"),
outfile = reference_file,
overwrite = TRUE
)
st_layers(reference_file)## Driver: GPKG
## Available layers:
## layer_name geometry_type features fields crs_name
## 1 divides Polygon 1122 5 NAD83 / Conus Albers
## 2 flowlines Line String 1129 19 NAD83 / Conus Albers
## 3 network NA 1145 23 <NA>Get some Points of Interest
There are many locations on the network (e.g. dams, gage, etc.) that we want to ensure are preserved in a network manipulation. That means that no matter how a fabric is refactored or aggregated, key hydrolocations persist. This is critical to ensuring cross dataset interoperability, consistent data streams for assimilation and model coupling, and persistent nexus locations.
For this example well read all hydrolocations from the community POI set (GFv20), convert them to spatial points and keep only those within the reference subset domain.
hf = read_hydrofabric(reference_file)
pois = open_dataset(glue("{source}/v2.2/conus_hl")) %>%
filter(hl_source == 'GFv20',
vpuid %in% unique(hf$flowpaths$vpuid),
hf_id %in% hf$flowpaths$id) %>%
collect() %>%
st_as_sf(coords = c("X", "Y"), crs = 5070)
make_map(reference_file, pois)Build a Refactored Fabric
The reference network provides the minimal discretization of the landscape and river network offered by this system. Derived from a traditional cartographic product, we need to remove small river segments that are to short for stable routing calculations and split long narrow catchments that have long flow paths. This process is known as refactoring and is describe in detail in the refactoring section here
refactored = refactor(
reference_file,
split_flines_meters = 10000,
collapse_flines_meters = 1000,
collapse_flines_main_meters = 1000,
pois = pois,
fac = '/vsis3/lynker-spatial/gridded-resources/fac.vrt',
fdr = '/vsis3/lynker-spatial/gridded-resources/fdr.vrt',
outfile = refactored_file
)
make_map(refactored_file, pois)Build an Aggregated Network
This next set of steps will run aggregation tools over the refactored
network. The process of aggregating to a Uniform Distribution.
The first step in doing this is to remap the hydrolocations we enforces
in the refactored fabric. With any refactor execution with
hydrofabric::refactor a lookup table is produced that
relates the original hydrofabric IDs to the new identifiers they became.
A quick join can provide a mapping of hydrolocations to the refactored
network. Passing these to the aggregate_* will ensure they are
not aggregated over in the processing.
hydrolocations = read_sf(refactored_file, 'lookup_table') %>%
inner_join(pois, by = c("NHDPlusV2_COMID" = "hf_id")) %>%
select(poi_id, NHDPlusV2_COMID, id = reconciled_ID) %>%
distinct()
head(hydrolocations)## # A tibble: 6 × 3
## poi_id NHDPlusV2_COMID id
## <int> <dbl> <int>
## 1 37345 2899997 2
## 2 37014 2899553 10
## 3 36913 2900669 15
## 4 36920 2900581 24
## 5 36914 2900571 28
## 6 36664 2898115 44
aggregate_to_distribution(
gpkg = refactored_file,
hydrolocations = hydrolocations,
ideal_size_sqkm = 10,
min_length_km = 1,
min_area_sqkm = 3,
outfile = aggregated_file,
overwrite = TRUE )
make_map(aggregated_file, pois)Generate a NextGen Network
In order to make this hydrofabric compliant with the NextGen flowline-to-nexus topology and mapping, we’ll run the following over our aggregated network.
unlink(nextgen_file)
apply_nexus_topology(aggregated_file, export_gpkg = nextgen_file)## [1] "tutorial/poudre_ng.gpkg"
hf = read_hydrofabric(nextgen_file)
make_map(nextgen_file, read_sf(nextgen_file, "nexus"))And there you have it! This is the minimal set of information needed in a NextGen hydrofabric!
Enriching the Network
Derive the divide-level data needed for CFE/NOM/PET
The catchments generated in the network preparation need land surface
attributes before we can run hydrologic simulations over them. This sort
of data typically includes things like soil type, average basin slope,
and land cover. The easiest way to accomplish this is to use to use use
climateR to
access data, and zonal to rapidly summarize gridded data to the POLYGON
scale. Both are core components of the NOAA-OWP/hydrofabric
meta package and should already be loaded!
vsi <- "/vsis3/lynker-spatial/gridded-resources"
div <- read_sf(nextgen_file, "divides")NOAH OWP Varibables
nom_vars <- c("bexp", "dksat", "psisat", "smcmax", "smcwlt")
r = rast(glue("{vsi}/nwm/conus/{nom_vars}.tif"), lyrs = seq(1,length(nom_vars)*4, by = 4))
modes = execute_zonal(r[[1]],
fun = mode,
div, ID = "divide_id",
join = FALSE) %>%
setNames(gsub("fun.", "", names(.)))
gm = execute_zonal(r[[2:3]],
fun = geometric_mean,
div, ID = "divide_id",
join = FALSE) %>%
setNames(gsub("fun.", "", names(.)))
m = execute_zonal(r[[4:5]],
fun = "mean",
div, ID = "divide_id",
join = FALSE) %>%
setNames(gsub("mean.", "", names(.)))
d1 <- power_full_join(list(modes, gm, m), by = "divide_id")GW Routing parameters
GW data comes form the routlink file (traditionally the
GWBUCKPARM_CONUS_FullRouting.nc in the NWM)
crosswalk <- as_sqlite(nextgen_file, "network") |>
select(hf_id, divide_id) |>
collect()
d2 <- open_dataset(glue("{source}/v2.2/reference/conus_routelink")) |>
select(hf_id , starts_with("gw_")) |>
inner_join(mutate(crosswalk, hf_id = as.integer(hf_id)), by = "hf_id") |>
group_by(divide_id) |>
collect() |>
summarize(
gw_Coeff = round(weighted.mean(gw_Coeff, w = gw_Area_sqkm, na.rm = TRUE), 9),
gw_Zmax_mm = round(weighted.mean(gw_Zmax_mm, w = gw_Area_sqkm, na.rm = TRUE), 9),
gw_Expon = mode(floor(gw_Expon))
)Forcing Downscaling base data
Tools like CIROH ngen-datastream and NextGen in a Box require forcings which are downscaled using attribuets like a catchment centroid, mean elevation, slope, and aspect.
- X, Y centroid
- Mean elevation and slope
- Circular mean of aspect.
X Y (for forcing downscaling)
d3 <- st_centroid(div) |>
st_transform(4326) |>
st_coordinates() |>
data.frame() |>
mutate(divide_id = div$divide_id)Elevation data for Forcing downscaling and NOAH-OWP
dem_vars <- c("elev", "slope", "aspect")
r <- rast(glue('{vsi}/250m_grids/usgs_250m_{dem_vars}.tif'))
d4 <- execute_zonal(r[[1:2]],
div, ID = "divide_id",
join = FALSE) |>
setNames(c("divide_id", "elevation_mean", " slope"))
d5 <- execute_zonal(r[[3]],
div, ID = "divide_id", fun = circular_mean,
join = FALSE) |>
setNames(c("divide_id", "aspect_c_mean"))
model_attributes <- power_full_join(list(d1, d2, d3, d4, d5), by = "divide_id")
write_parquet(model_attributes, model_atts_file)Forcing Weight Grids
type = "medium_range.forcing"
w = weight_grid(rast(glue('{vsi}/{type}.tif')), div, ID = "divide_id") |>
mutate(grid_id = type)
head(w)
write_parquet(w, model_weights_file)Extacting Cross Sections
hyperlink to bew 3D-vignette link to JOSS paper
crosswalk <- as_sqlite(nextgen_file, "network") |>
select(hf_id, id, divide_id, hydroseq, poi_id) |>
filter(!is.na(poi_id)) %>%
collect() %>%
slice_min(hydroseq)
open_dataset(glue("{source}/v2.2/reference/conus_routelink/")) |>
select(hf_id, starts_with("ml_")) ## FileSystemDataset (query)
## hf_id: int32
## ml_tw_inchan_m: double
## ml_tw_bf_m: double
## ml_y_inchan_m: double
## ml_y_bf_m: double
## ml_ahg_c: double
## ml_ahg_f: double
## ml_ahg_a: double
## ml_ahg_b: double
## ml_ahg_k: double
## ml_ahg_m: double
## ml_r: double
## ml_bf_channel_area_m2: double
## ml_inchan_channel_area_m2: double
## ml_bf_channel_perimeter_m: double
## ml_inchan_channel_perimeter_m: double
## ml_roughness: double
## ml_hf_source: string
##
## See $.data for the source Arrow object
(cs <- open_dataset(glue("{source}/v2.2/reference/conus_routelink/")) |>
select(hf_id, ml_y_bf_m, ml_tw_bf_m, ml_r) %>%
inner_join(mutate(crosswalk, hf_id = as.integer(hf_id)), by = "hf_id") |>
collect() %>%
summarise(TW = mean(ml_tw_bf_m),
r = mean(ml_r),
Y = mean(ml_y_bf_m),
poi_id = poi_id[1]))## # A tibble: 1 × 4
## TW r Y poi_id
## <dbl> <dbl> <dbl> <chr>
## 1 19.6 36.3 1.48 35836
bathy = AHGestimation::cross_section(r = cs$r, TW = cs$TW, Ymax = cs$Y)
plot(bathy$x, bathy$Y, type = "l",
ylab = "Releative distance (m)",
xlab = "Depth (m)",
main = glue("Average XS at POI: {cs$poi_id}"))
library(plotly)
crosswalk <- as_sqlite(nextgen_file, "network") |>
select(hf_id, id, toid, divide_id, hydroseq, poi_id) |>
collect() %>%
slice_max(hydroseq)
cw = open_dataset(glue('{source}/v2.1.1/nextgen/conus_network')) %>%
semi_join(crosswalk, by = "hf_id") %>%
collect()
message(sum(cw$lengthkm), " kilometers of river")
open_dataset(glue('{source}/v2.1.1/nextgen/conus_xs')) %>%
filter(vpuid %in% unique(cw$vpuid), hf_id %in% unique(cw$id)) %>%
group_by(hf_id, cs_id) %>%
collect() %>%
mutate(uid = cur_group_id()) %>%
plot_ly(x = ~X, y = ~Y, z = ~Z, split = ~as.factor(uid),
type = 'scatter3d', mode = 'markers+lines',
line = list(width = 3), marker = list(size = 2)) %>%
layout(list(aspectmode='manual',
aspectratio = list(x=100, y=100, z=1)),
showlegend = FALSE)Populate Flowpath Attributes
Slowly the ML enhanced DEM-based cross sections are being used to
supplement a national Routelink file (conus_routelink) that
is complete with the routing attributes need for both t-route and wrfhydro / NWM to
execute. We are striving to implement the routwlink file at the
reference fabric level meaning it can be expetend to
any derived product. As such, the length average contribution of each
reference flowline to its aggregated flowpath needs to be calculated.
This can be done in the following way:
add_flowpath_attributes(nextgen_file, source = source)## [1] "tutorial/poudre_ng.gpkg"## # A tibble: 6 × 13
## fid id rl_Qi_m3s rl_MusX rl_n rl_So rl_ChSlp rl_BtmWdth_m
## <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 wb-1 0 0.2 0.06 0.0197 0.517 3.91
## 2 2 wb-10 0 0.2 0.0565 0.0481 0.420 9.14
## 3 3 wb-100 0 0.2 0.06 0.0971 0.641 2.33
## 4 4 wb-101 0 0.2 0.06 0.064 0.634 2.39
## 5 5 wb-102 0 0.2 0.06 0.0726 0.628 2.45
## 6 6 wb-103 0 0.2 0.06 0.0552 0.679 2.05
## # ℹ 5 more variables: rl_Kchan_mmhr <dbl>, rl_nCC <dbl>, rl_TopWdthCC_m <dbl>,
## # rl_TopWdth_m <dbl>, length_m <dbl>Adding GPKG Symbology
append_style(nextgen_file, layer_names = c("divides", "flowpaths", "nexus"))## [1] "tutorial/poudre_ng.gpkg"